code: def add_left_padding(input_ids, max_length, pad_value=0):
      """Adds left padding to the input_ids tensor.
  
      Args:
          input_ids (torch.Tensor): The input tensor.
          max_length (int): The maximum length of the tensor after padding.
          pad_value (int, optional): The value used for padding. Defaults to 0.
  
      Returns:
          torch.Tensor: The input_ids tensor with left padding.
  
      Example:
          >>> input_ids = torch.tensor([1, 2, 3])
          >>> max_length = 5
          >>> padded_tensor = add_left_padding(input_ids, max_length)
          >>> padded_tensor
          tensor([0, 0, 1, 2, 3])
      """
      padding = torch.tensor(
          [pad_value] * (max_length - input_ids.shape[0]),
          dtype=torch.int64,
          device=input_ids.device,
      )
      return torch.cat((padding, input_ids), dim=-1)
  
imports: import copy
  import logging
  import tempfile
  from typing import Dict, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn.functional
  import transformers
  from bitsandbytes.nn.modules import Embedding
  from packaging import version
  from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, TextStreamer
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.schema.trainer import LLMTrainerConfig
  from ludwig.utils.error_handling_utils import default_retry
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.model_utils import find_embedding_layer_with_path
label_desc: Use image node_id 7 for calling a global function with example usage: add_left_padding(input_ids, max_length, pad_value) and returns: torch